US5887035A - Method for joint equalization and detection of multiple user signals - Google Patents

Method for joint equalization and detection of multiple user signals Download PDF

Info

Publication number
US5887035A
US5887035A US08962249 US96224997A US5887035A US 5887035 A US5887035 A US 5887035A US 08962249 US08962249 US 08962249 US 96224997 A US96224997 A US 96224997A US 5887035 A US5887035 A US 5887035A
Authority
US
Grant status
Grant
Patent type
Prior art keywords
symbol
vector
estimated
transmitted
probability
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Lifetime
Application number
US08962249
Inventor
Karl J. Molnar
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Unwired Planet LLC
Original Assignee
Ericsson Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Grant date

Links

Images

Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00Baseband systems
    • H04L25/02Details ; Arrangements for supplying electrical power along data transmission lines
    • H04L25/03Shaping networks in transmitter or receiver, e.g. adaptive shaping networks ; Receiver end arrangements for processing baseband signals
    • H04L25/03006Arrangements for removing intersymbol interference
    • H04L25/03178Arrangements involving sequence estimation techniques
    • H04L25/03331Arrangements for the joint estimation of multiple sequences
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00Baseband systems
    • H04L25/02Details ; Arrangements for supplying electrical power along data transmission lines
    • H04L25/03Shaping networks in transmitter or receiver, e.g. adaptive shaping networks ; Receiver end arrangements for processing baseband signals
    • H04L25/03006Arrangements for removing intersymbol interference
    • H04L25/03178Arrangements involving sequence estimation techniques
    • H04L25/03248Arrangements for operating in conjunction with other apparatus
    • H04L25/03292Arrangements for operating in conjunction with other apparatus with channel estimation circuitry

Abstract

An iterative approach is used to find maximum likelihood sequence estimates of a transmitted symbol sequence. Each possible transmitted signal is represented by an indicator vector which selects the transmitted symbol from a symbol library vector. Channel estimates are generated representing the estimated impulse response of the communication channel to each transmitted symbol. The channel estimates are used to form a matched medium response vector and an interaction matrix. The matched medium response vector represents the matched medium response of the receiver to both a transmitter and the channel for each transmitted symbol. The interaction matrix represents the ISI between symbols. The matched medium response vector and the interaction matrix are fed to an estimated symbol probability vector generator which calculates in an iterative manner the estimated symbol probability vectors corresponding to each transmitted symbol. The estimated symbol probability vectors can be output to a hard-decision generator which makes a hard-decision about the transmitted symbols. Alternatively, the estimated symbol probability vectors can be output to a soft-decision decoder.

Description

FIELD OF THE INVENTION

The present invention relates generally to signal processing in a telecommunications system, and, more particularly, to methods for joint equalization and detection of symbol sequences.

BACKGROUND OF THE INVENTION

The basic function of a communication system is to send information from a source that generates the information to one or more destinations. In a radio communication system, a number of obstacles must be overcome to successfully transmit and receive information including channel distortion which may cause, for example, intersymbol interference (ISI) and additive noise. The receiver must compensate for the effects of channel distortion and the resulting intersymbol interference. This is usually accomplished by means of an equalizer/detector at the receiver.

A number of different equalization schemes have been used in the past to eliminate or minimize intersymbol interference. The most commonly used approaches include decision feedback equalization (DFE) and maximum likelihood sequence estimation (MLSE). In a maximum likelihood sequence estimation equalization scheme, a detector produces the most probable symbol sequence for the given received sample sequence . An algorithm for implementing maximum likelihood sequence detection is the Viterbi algorithm, which was originally devised for decoding convolutional codes. The application of the Viterbi algorithm to the problem of sequence detection in the presence of ISI is described by Gottfried Ungerboeck in "Adaptive Maximum Likelihood Receiver for Carrier Modulated Data Transmission Systems," IEEE Transactions on Communications, Volume COM-22, #5, May 1974. This MLSE approach employs a matched filter followed by a MLSE algorithm and an auxiliary channel estimation scheme.

A major drawback of using maximum likelihood sequence detection for communications channels which may experience intersymbol interference is that the computational complexity grows exponentially as a function of the span of the intersymbol interference and the number of interfering users. Consequently, maximum likelihood sequence detection is practical only for single user signals where the intersymbol interference spans only a few symbols. Therefore, there is great interest in finding new approaches to sequence estimation which reduce the computational complexity associated with maximum likelihood sequence detection.

SUMMARY OF THE INVENTION

The present invention provides an iterative method for generating estimates of a transmitted symbol sequence. The transmitted symbol sequence is received over a communication channel which may result in distortion of the waveform. Each possible transmitted symbol is represented by an indicator vector which is used to select a possible transmitted symbol from a symbol library vector. An estimate of the indicator vector, called the estimated symbol probability vector, is calculated and used to select an estimate of the transmitted symbol.

The t'th element of the estimated symbol probability vector is an estimate of the probability that the t'th symbol in the symbol library vector was transmitted. To form the estimated symbol probability vector, channel estimates are generated representing the estimated impulse response of the channel for each transmitted symbol. The channel estimates are used to form a matched medium response vector and an interaction matrix.

The matched medium response vector is formed by a combination of the received signal, the channel estimates, and the symbol library vector and represents the matched medium response component for the possible transmitted symbols in the symbol library vector. The interaction matrix is based upon the channel estimates and the symbol library vector and contains the interaction between interfering symbols. The matched medium response vector and the interaction matrix are used to derive the estimated symbol probability vector for each symbol in the transmitted symbol sequence. The estimated symbol probability vector is used to select an estimate of each transmitted symbol in said symbol sequence from the symbol library vector.

The estimated symbol probability vector is formed by recursively estimating each element of each estimated symbol probability vector based upon the matched medium response vector, the interaction matrix and a previous estimate of the estimated symbol probability vector. The recursive estimation of each element of each estimated symbol probability vector is performed until some pre-defined convergence criteria is satisfied or for a pre-determined number of iterations. The recursive nature of the estimation process results in maximum a posteriori estimates of the indicator vector which may be used to select an estimate of the transmitted symbol from the symbol library vector.

The primary advantage of the present invention is a reduction in overall complexity. Using the iterative approach of the present invention, the state space is of the order (Nu ×Nb) as opposed to Nb Nu in the conventional Viterbi algorithm. This reduction in overall complexity is at the expense of using multiple iterations in the algorithm. The reduction in computational complexity allows for joint demodulation of multiple user signals simultaneously.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a digital communication system.

FIG. 2 is a block diagram of a detector in accordance with the present invention.

FIG. 3 is a block diagram of an estimated indicator vector generator.

FIG. 4 is a flow diagram illustrating the detection method of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates a radio transmission system for digital signal transmission. The system consists of a transmitter 12 having an impulse response F(t), a communication channel 14 with additive white Gaussian noise (AWGN), a receiver 16 with impulse response f(t), a sampler 18 that periodically samples the output of the receiver 16, and a symbol detector 20. In general, the transmitter 12 receives an input data sequence which represents information to be transmitted. The input data sequence is assumed to be in digital form. The transmitter 16 modulates a carrier signal with the input sequence using a M-ary modulation scheme such as pulse amplitude modulation (PAM) or quadrature amplitude modulation (QAM). The input data sequence is sub-divided into k bit symbols, and each symbol is represented by a distinct signal waveform referred to herein as the transmitted signal. The transmitted signal is subjected to disturbances in the communication channel 14 which may result in phase and/or amplitude distortion. The transmitted signal is also degraded by added noise. The channel corrupted signal is received at the receiver 16. The signal received at the receiver is referred to herein as the received signal yi.

The receiver 16 has an impulse response f(t) which is ideally matched to the impulse response F(t) of the transmitter 12. In actual practice, the ideal receiver 16 may not be realizable, so an approximation of the ideal receiver 16 may be used. The output of the receiver 16 is sampled periodically by the sampling circuit 18. A timing signal extracted from the received signal yi is used as a clock for sampling the received signal yi. The sampled and filtered received signal yi is passed to the detector 20. The purpose of the detector 20 is to estimate a transmitted sequence sm which is contained in the received signal yi.

In a digital communication system that transmits information over a channel that causes ISI, the optimum detector is a maximum likelihood symbol sequence detector (MLSD) which produces the most probable symbol sequence for a given received symbol sequence. A decision rule is employed based on the computation of the posterior probabilities defined as

P(s.sub.m |y.sub.i), m=1,2, . . . , M             (1)

where sm is the transmitted sequence, and y0i is the received signal. The decision criterion is based on selecting the sequence corresponding to the maximum of the set of posterior probabilities {P(sm)}. This criterion maximizes the probability of a correct decision and, hence, minimizes the probability of error. This decision criterion is called the maximum a posteriori probability (MAP) criterion.

Using Bayes rule, the posterior probabilities may be expressed as ##EQU1## where f(yi |sm) is a conditional probability density function (p.d.f.) of the observed vector y given the transmitted sequence sm, and P(sm) is the a priori probability of the mth signal being transmitted. Computation of the posterior probabilities P(sm |yi) requires knowledge of the a priori probabilities P(sm) and the conditional p.d.f. f(yi |sm) for m=1,2, . . . ,M. The conditional p.d.f. is called the likelihood function.

Some simplification occurs in the MAP criterion when the M signals are equally probable a priori, i.e., P(sm)=1/M for all M. Furthermore, it should be noted that the denominator in Equation (2) is independent of which signal of the M signals is transmitted. Consequently, the decision rule based on finding the signal that maximizes the posterior probability P(sm |yi) is equivalent to finding the signal that maximizes the likelihood function f(yi |sm).

In the case of a faded and dispersive channel with additive, white Guassian noise AWGN, the received signal is assumed to be in the form: ##EQU2## where yi is the received signal at time i, sl is the l'th transmitted symbol, gi-l,l is a channel impulse response that symbol s. contributes to the received signal at time i, and wi is the noise at time i. This model corresponds to a received signal yi having symbol-spaced echoes and Nyquist matched filtering.

A conventional log-likelihood function used in the receiver is written as follows: ##EQU3##

The term rl represents a matched medium response of the receiver to both the transmitter and the channel for symbol l. The term hl,m represents the complex ISI between adjacent symbols generated by the multipath component and the pulse-shape waveform. The symbol g,i-l,lH is the conjugate transpose of the channel impulse response .

The method of the present invention represents the l'th symbol, sl, received during a given sampling interval, as a function of two vectors which can be described mathematically as follows:

s.sub.l =Z.sub.l.sup.T B.sub.l                             (7)

where Zl is an indicator vector and Bl is a symbol library vector. It should be noted that the symbol library vector Bl may be fixed for all values of l, in which case the modulation does not vary over time (e.g. QPSK). The symbol library vector Bl could be varied over time to represent a coded modulation sequence. The symbol library vector Bl contains as elements all possible transmitted symbols. The indicator vector Zl contains a plurality of elements consisting of a single element equal to "1" and all remaining elements equal to "0". The number of elements in the indicator vector Zl is equal to the number of elements (or symbols) in the symbol library vector Bl. Consequently, the position of the "1" element within the indicator vector uniquely identifies a symbol which is located at the same position in the symbol library vector Bl For example, an indicator vector with a "1" element in the 3'rd position indicates that the 3'rd element of the symbol library vector Bl was transmitted.

Using indicator vector notation and substituting the variables U and V, the log-likelihood function of equation (4) becomes: ##EQU4## is a matched medium response vector for the possible transmitted symbols bl ; and

V.sub.l,m =B.sub.l.sup.* B.sub.l.sup.T h.sub.l,m           (10)

is an an interaction matrix that represents the ISI between the received symbols and their neighbor symbols. The term neighbor symbols is used herein to mean symbols in the local neighborhood of a given received symbol which induce ISI in the given received symbol. As will be described below, the log-likelihood function of equation (8) is used as a convergence criteria in the iterative calculation of the estimated symbol probability vector Zl for a given received symbol sl.

In accordance with the present invention, an iterative approach is used to find an estimate Zl of the indicator vector Zl for each transmitted symbol. This estimate is referred to herein as the estimated symbol probability vector Zl and has t'th element Zl,l.sup.(p) given by the equation: ##EQU5## where el represents an indicator vector having a "1" as the value of the t'th element, while all other elements have a value of "0". The term Zm.sup.(p-1) represents estimated symbol probability vectors for the previous iteration of the neighbor (i.e. interfering) symbols. Nb is the number of possible symbols in the symbol library vector Bl and nl defines the estimated indicator vectors Zm in the local neighborhood of sl. The numerator of equation (11) is referred to as the partial metric parameter and the denominator is referred to as the normalizing parameter.

The approach to calculating the estimated symbol probability vector Zl.sup.(p) and hence the received symbol sl, is recursive or iterative in nature to account for the interference from adjacent sampling intervals, also known as ISI. ISI, by definition, implies that a given sampling interval not only contains information related to the symbol directly associated with that sampling interval, but also contains information related to symbols received in adjacent sampling intervals. Consequently, in order to accurately estimate the value of a symbol at a given sampling interval, knowledge of the contents, or estimated contents, of adjacent sampling intervals is also required. Equation (11) defines a recursive relation between the estimate of the symbol probability vector for a particular symbol at the p'th iteration and the estimated symbol probability vectors corresponding to the interfering neighbors of that symbol at the previous (p-1)'th iteration.

In the initial iteration (p=1) of equation (11), an assumed value for each element of each estimated symbol probability vector Zm.sup.(o) is used. After the first iteration (p>1), the estimated symbol probability vectors Zl from the (p-1)'st iteration are used in the calculation and are designated in equation (11) as Zm. Each iteration results in an updated set of estimated symbol probability vectors Zl, one for each symbol being estimated. This process is repeated for a pre-determined number of iterations or until some pre-defined convergence criteria is satisfied.

In the iterative calculation of estimated symbol probability vectors Zl, some function f(Zm.sup.(p-1))of the previous iteration of the estimated symbol probablity vectors Zm.sup.(p-1) in equation (11) could be used in place of the estimated symbol probablity vectors Zm.sup.(p-1). This function f(Zm.sup.(p-1) may output a hard decision in which each element of the estimated symbol probablity vector Zm.sup.(p-1) takes on a value of 0 or 1. Alternatively, the function f(Zm.sup.(p-1)) could be a - continuous non-linear function that produces an output for each element of the estimated -symbol probablity vector Zm.sup.(p-1) in the range from 0 to 1, where the sum across all elements is exactly 1.

As described above, during each iteration a complete set of estimated symbol probability vectors Zl must be calculated. Consequently, application of equation (11) is required Nb times for each of the L members of the estimated symbol probability vector Zl. That is, since there are L sampling intervals under consideration, there are consequently L distinct estimated symbol probability vectors, Zl through Zl, with each vector containing as many elements as there are symbols in the symbol library, Nb. At the conclusion of each iteration, there will be a set of estimated symbol probability vectors corresponding to each of the transmitted symbols. A determination is then made whether an additional iteration is required. If so, the calculations of the estimated symbol probability vectors is repeated resulting in updated estimates. This process is repeated for a predetermined number of iterations or until some pre-defined convergence criteria is satisfied.

The log-likelihood equation (8) may, for example, be used to test for convergence. After each iteration, the entire set of estimated symbol probability vectors {Z1,Z2, . . . ZL } is applied to the log-likelihood equation (8) resulting in a numerical value which reflects the likelihood that the set of estimated symbol probability vectors {Z1,Z2, . . . ZL }, and more specifically the symbols that they represent, accurately reflect the actual symbols sent by the transmitter. When further iteration is deemed unnecessary, the set of estimated symbol probability vectors {Z1,Z2, . . . ZL } can be queried as to their contents. The individual elements of the estimated symbol probability vectors Zl may take on real values ranging from 0 to 1, and reflect the probabilities that the corresponding symbols in the associated symbol library vector Bl were transmitted. These probabilities may then be used to assist in performing either a soft-decision or a hard decision with regard to the symbol or symbol sequence in question.

To those skilled in the art it will become apparent that the detection method of the present invention, which is described above for the case of a single user signal, can easily be extended to include the case of multiple user signal. An implementation of the multiple user configuration contemplated would necessarily require minor alterations to the mathematical expressions previously described for the single user configuration. However, the basic functional intent of these expressions remains unchanged.

Specifically, in the multiple user instance, equation (3) would appear as follows: ##EQU6## where an additional summation over the entire user population, Nu, has been added. Likewise, equation (4) assumes the following form in the multiple user scenario: ##EQU7## In this case, sk,u is the l'th symbol for user u, and gk-l,l (u) is a channel impulse response contributing to the received signal at time l and delay k-l for the user u. The term hl.m (u,r) represents the interaction between users u and v. Once again, only additional summations over the user population, Nu, have been added.

When converted to indicator vector form, the log-likelihood equations becomes: ##EQU8## The estimated symbol probability equation becomes: ##EQU9##

FIG. 2 illustrates the detector 20 for detecting a received signal yi from multiple users. The detector 20 includes a channel estimator 22, a matched medium response vector generator 24, and a symbol interaction matrix generator 30. The channel estimator 22 generates an estimate of the channel impulse response gi-l,l based on the received signal yi. The term gi-l,l represents the channel impulse response corresponding to the l'th symbol with a delay factor of i-l. The estimate of the channel impulse response gi-l,l is computed in a known manner. Known training symbols can be used to generate channel estimates. Alternatively, hard or soft detected symbols can also be used to generate channel estimates. The estimate of the channel impulse response gi-l,l is passed to the matched medium response vector generator 24 and the _symbol interaction matrix generator 30.

The matched medium response vector generator 24 includes a matched medium response filter 26 and a vector generator 28. The matched medium response filter 26 computes -the matched medium response rl,u to both the transmitter and the channel medium for symbol l for each user u given the received signal y0i and the estimate of the channel impulse response gi-l,l (u). The matched medium response rl,u is then passed to the vector generator 28, which in conjunction with knowledge of a symbol library 36 computes a matched medium response vector, Ul,u.

The symbol interaction matrix generator 30 includes an interaction coefficient generator 32 and a matrix generator 34. The interaction coefficient generator 32 computes the symbol interaction coefficient, hl,m (U,v), which is indicative of the channel response for symbols l and m for each user pair u,v. The symbol interaction coefficient hl,m (u,v) is passed to the matrix generator 34 which, in coordination with information in the symbol library 36, generates the symbol interaction matrix, Vl,m. The matched medium response vector, Ui,u, and the symbol interaction matrix, Vl,m (u,v), are finally passed to an estimated symbol probability vector generator 40, which computes the estimated symbol probability vector, Zl in accordance with equation (16).

Shown in FIG. 3 is a more detailed schematic diagram of the estimated symbol probability vector generator 40. The estimated symbol probability vector generator 40 is comprised of an iterative processor 42, an iteration and output controller 46, and a previous iteration buffer 44. The iterative processor 42 is responsible for implementing and solving the mathematical expressions that describe and define the estimated symbol probability vector, Zl,u, for a given symbol, l and user u in the multi-user embodiment. The output of the iterative processor 42 is an estimated indicator vector, Zl,u, which is received and analyzed by the iteration and output controller 46.

The controller 46 implements a particular strategy for determining whether an additional iteration is required. Potential iteration control strategies could include defining a fixed number of iterations, or the observing and monitoring a Z dependent function, such as the log-likelihood function previously described. In such a case, the decision regarding the need for an additional iteration could be determined by observing the mean squared error between successively computed Zl vectors. Should the controller 46 determine that an additional iteration is required, the newly computed set of estimated symbol probability vectors, {Z1,Z2, . . . Zl }, is temporarily stored in the iteration buffer 44. The set of estimated symbol probability vectors {Z1,Z2, . . . Zl } stored in the iteration buffer 44 are then used in the computation of estimated symbol probability vectors during the next iteration.

Regardless of the iteration control strategy implemented, the function of the controller 46 is to administer the implemented iteration termination criteria, and ultimately output the iteratively optimized estimated symbol probability vector, Zl,u The estimated symbol probability vector Zl,u may be used by a hard-decision generator 48 to make a hard-decision about estimated symbol probability vector Zl,u into estimated symbol sl,u The estimated symbol sl,u is then output from the hard-decision generator 48 to a decoder. Alternatively, the estimated symbol probability vector Zl,u may be passed directly to a soft-decision decoder.

FIG. 4 is a flow diagram illustrating the operation of the detector 20 of the present invention. After receiving the received signal yi, the detector 20 computes channel estimates gi-l,l which represent the estimated impulse response of the communication channel to the transmitted symbols (block 100). The detector 20 then computes the matched medium response vector Ul,u (block 102) and the symbol interaction coefficients matrix Vl,u (block 104). The matched medium response vector Ul,u is calculated based on the channel estimates gi-l,l and the received signal yi. The symbol interaction coefficients matrix Vl,u is computed based on the channel estimates gi-l,l and represents the interaction between adjacent symbols. The matched medium response vectors Ul,u and the symbol interaction coefficient matrix Vl,u are then used to compute estimated symbol probability vectors {Z0,Z1,Z2 . . . } for each received symbol (block 106). The log-likelihood for the set of estimated symbol probability vectors is then calculated using the log-likelihood equation (block 108). The log-likelihood results in a numerical value which reflects the likelihood that the set of estimated symbol probability vectors {Z0,Z1,Z2 . . . } accurately represents the set of transmitted symbols. The detector 20 then determines whether another iteration is required (block 110), using, for example, some pre-determined convergence criteria. If so, a new set of estimated symbol probability vectors for each received symbol are computed (block 106). The log likelihood based on this new set of estimated symbol probability vectors is then calculated (block 108). This process is repeated until the detector 20 determines that no further iterations are required (block 110). The final set of estimated symbol probability estimates are then used to generate hard decision estimates of the transmitted symbols, or, alternatively, are output to a soft decision decoder (block 112).

Based on the foregoing, it is seen that the present invention provides an alternative approach to finding maximum likelihood sequence estimates which may be considered when the state space is too large for a conventional Viterbi detector. The present invention may, of course, be carried out in other specific ways than those herein set forth without departing from the spirit and essential characteristics of the invention. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, and all changes coming within the meaning and equivalency range of the appended claims are intended to be embraced therein.

Claims (19)

What is claimed is:
1. A method for demodulating a received signal corresponding to a transmitted symbol sequence having a plurality of symbols comprising the steps of:
a) receiving the signal over a communication channel;
b) forming a symbol library vector representing possible transmitted symbols in the transmitted symbol sequence;
c) generating channel estimates representing the estimated impulse response of the communication channel to each transmitted symbol;
d) forming a matched medium response vector based upon the received signal, the channel estimates, and the symbol library vector;
e) forming an interaction matrix based upon the channel estimates and the symbol library vector; and
f) forming an estimated symbol probability vector having a plurality of elements for selecting an estimate of each transmitted symbol in the transmitted symbol sequence, wherein the estimated symbol probability vector is formed by recursively estimating each element of each estimated symbol probability vector based upon the matched medium response vector, the interaction matrix, and a previous estimate of the estimated symbol probability vector for interfering symbols.
2. The demodulation method according to claim 1 wherein the recursive estimation of each element of each estimated symbol probability vector is performed until a pre-defined convergence criteria is satisfied.
3. The demodulation method according to claim 1 wherein the recursive estimation of each element of each estimated symbol probability vector is performed for a pre-determined number of iterations.
4. The demodulation method according to claim 1 wherein the estimated symbol probability vector is output as a soft decision vector.
5. The demodulation method according to claim I further including the step of selecting a symbol from the symbol library vector based upon the estimated symbol probability vector and outputting the selected symbol as a hard decision.
6. The demodulation method according to claim 1 further including the step of regenerating the channel estimates during the iterative calculation of the estimated symbol probability vectors.
7. The demodulation method according to claim 1 wherein a function of the previous estimated symbol probability vector is used to calculate the estimate for the symbol probability vector on the next iteration.
8. A method for demodulating multiple user signals each of which corresponds to a transmitted symbol sequence having a plurality of symbols, said demodulation method comprising the steps of:
a) receiving said multiple user signals over a communication channel;
b) forming a symbol library vector for each user signal representing possible transmitted symbols in the transmitted symbol sequence;
c) generating channel estimates representing estimated impulse responses of the communication channel to each transmitted symbol in each of the user signals;
d) forming a matched medium response vector for each of the user signals based upon the user signal, the corresponding channel estimates and the corresponding symbol library vectors;
e) forming an interaction matrix for each of the user signals based upon the corresponding channel estimates and the symbol library vector; and
f) forming estimated symbol probability vectors having a plurality of elements for selecting an estimate of each transmitted symbol in each of the user signals, wherein said estimated symbol probability vectors are formed by recursively estimating each element of each of the estimated symbol probability vectors based upon the matched medium response vector, the interaction matrix, and a previous estimate of the estimated symbol probability vector for interfering symbols.
9. The demodulation method according to claim 8 wherein each element of the estimated symbol probability vectors is calculated recursively based upon a previous estimate of the estimated symbol probability vector.
10. The demodulation method according to claim 8 wherein the recursive estimation of each element of each of the estimated symbol probability vectors is performed until a predefined convergence criteria is satisfied.
11. The demodulation method according to claim 8 wherein the recursive estimation of each element of each of the estimated symbol probability vectors is performed for a predetermined number of iterations.
12. The demodulation method according to claim 8 wherein the estimated symbol probability vector is output as a soft decision vector.
13. The demodulation method according to claim 8 further including the step of selecting a symbol from the symbol library vector based upon the indicator vector and outputting the selected symbol as a hard decision.
14. The demodulation method according to claim 8 further including the step of regenerating the channel estimates during the iterative calculation of the estimated symbol probability vectors.
15. The demodulation method according to claim 8 wherein a function of the previous estimated symbol probability vector is used to calculate the estimate for the symbol probability vector on the next iteration.
16. A method for demodulating a received signal corresponding to a transmitted symbol sequence having a plurality of symbols comprising the steps of:
a) receiving a signal over a communication channel;
b) forming a symbol library vector representing possible transmitted symbols in the transmitted symbol sequence;
c) generating channel estimates representing the estimated impulse response of the communication channel to each transmitted symbol;
d) forming a matched medium response vector based upon the received signal, the channel estimates, and the symbol library vector;
e) forming an interaction matrix based upon the channel estimates and the symbol library vector; and
f) forming an estimated symbol probability vector based upon said matched medium response vector and said interaction matrix for selecting an estimate of each transmitted symbol, wherein said estimated symbol probability vector includes a plurality of elements corresponding to the elements of the symbol library vector, and wherein each element of the estimated symbol probability vector represents the probability of the corresponding element in the symbol library vector being the transmitted symbol.
17. The demodulation method according to claim 16 wherein the estimated symbol probability vector is output as a soft decision vector.
18. The demodulation method according to claim 16 further including the step of selecting a symbol from said symbol library vector based upon said indicator vector and outputting said selected symbol as a hard decision.
19. The demodulation method according to claim 16 further including the step of regenerating the channel estimates during the iterative calculation of the estimated symbol probability vectors.
US08962249 1997-10-31 1997-10-31 Method for joint equalization and detection of multiple user signals Expired - Lifetime US5887035A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US08962249 US5887035A (en) 1997-10-31 1997-10-31 Method for joint equalization and detection of multiple user signals

Applications Claiming Priority (10)

Application Number Priority Date Filing Date Title
US08962249 US5887035A (en) 1997-10-31 1997-10-31 Method for joint equalization and detection of multiple user signals
TW87116608A TW406505B (en) 1997-10-31 1998-10-07 Method for joint equalization and detection of multiple user signals
AU1115099A AU747161B2 (en) 1997-10-31 1998-10-22 Method for joint equalization and detection of multiple user signals
DE1998639967 DE69839967D1 (en) 1997-10-31 1998-10-22 Process for the simultaneous equalization and detection of multiple user signals
KR20007004721A KR20010031665A (en) 1997-10-31 1998-10-22 Method for joint equalization and detection of multiple user signals
EP19980953895 EP1029409B1 (en) 1997-10-31 1998-10-22 Method for joint equalization and detection of multiple user signals
CN 98810801 CN1278382A (en) 1997-10-31 1998-10-22 Method for joint equalization and detection of multiple user signals
JP2000519531A JP4329974B2 (en) 1997-10-31 1998-10-22 Combined equalization and detection method of a multi-user signal
CA 2308173 CA2308173A1 (en) 1997-10-31 1998-10-22 Method for joint equalization and detection of multiple user signals
PCT/US1998/022402 WO1999023795A1 (en) 1997-10-31 1998-10-22 Method for joint equalization and detection of multiple user signals

Publications (1)

Publication Number Publication Date
US5887035A true US5887035A (en) 1999-03-23

Family

ID=25505601

Family Applications (1)

Application Number Title Priority Date Filing Date
US08962249 Expired - Lifetime US5887035A (en) 1997-10-31 1997-10-31 Method for joint equalization and detection of multiple user signals

Country Status (8)

Country Link
US (1) US5887035A (en)
EP (1) EP1029409B1 (en)
JP (1) JP4329974B2 (en)
KR (1) KR20010031665A (en)
CN (1) CN1278382A (en)
CA (1) CA2308173A1 (en)
DE (1) DE69839967D1 (en)
WO (1) WO1999023795A1 (en)

Cited By (30)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1999055047A1 (en) * 1998-04-17 1999-10-28 Trustees Of Tufts College Blind adaptive equalization using cost function
US6044111A (en) * 1996-04-12 2000-03-28 U.S. Philips Corporation Equalizer with a sequence estimation method with state reduction for a receiver in a digital transmission system
US20020007257A1 (en) * 1999-11-04 2002-01-17 Eilon Riess Reliable symbols as a means of improving the performance of information transmission systems
US6345076B1 (en) * 1998-05-30 2002-02-05 U.S. Phillips Corporation Receiver for a digital transmission system
WO2002021784A1 (en) * 2000-09-06 2002-03-14 Motorola Inc. Soft-output error-trellis decoder for convolutional codes
US20020037062A1 (en) * 1999-11-04 2002-03-28 Eilon Riess Reliable symbols as a means of improving the performance of information transmission systems
US20020080896A1 (en) * 1999-11-04 2002-06-27 Verticalband, Limited Fast, blind equalization techniques using reliable symbols
US20020114408A1 (en) * 2000-12-21 2002-08-22 Nokia Corporation Method for channel equalization, a receiver a channel equalizer, and a wireless communication device
US6512802B1 (en) * 1999-09-28 2003-01-28 Nortel Networks Limited Method and apparatus for equalization and data symbol detection for MPSK modulation
US20030112775A1 (en) * 2001-12-14 2003-06-19 Molnar Karl James Method and apparatus for two-user joint demodulation in a system having transmit deversity
US6603823B1 (en) * 1999-11-12 2003-08-05 Intel Corporation Channel estimator
US6611494B1 (en) * 1999-05-19 2003-08-26 Agere Systems Inc. Orthogonal sequence generator
US20040032347A1 (en) * 2002-04-26 2004-02-19 Masato Yamazaki Soft-output decoder with computation decision unit
US6771722B2 (en) * 1998-07-31 2004-08-03 Motorola, Inc. Channel estimator and method therefor
US20050111347A1 (en) * 2002-05-10 2005-05-26 Marco Breiling Transmitting device and receiving device
US20090228766A1 (en) * 2008-03-06 2009-09-10 Nec Laboratories America, Inc. Simulatenous PMD Compensation and Chromatic Dispersion Compensation Using LDPC Coded OFDM
US20090303968A1 (en) * 2008-06-09 2009-12-10 Qualcomm Incorporation Increasing capacity in wireless communications
US20100029213A1 (en) * 2008-08-01 2010-02-04 Qualcomm Incorporated Successive detection and cancellation for cell pilot detection
US20100029262A1 (en) * 2008-08-01 2010-02-04 Qualcomm Incorporated Cell detection with interference cancellation
US20100046595A1 (en) * 2008-08-19 2010-02-25 Qualcomm Incorporated Semi-coherent timing propagation for geran multislot configurations
US20100046660A1 (en) * 2008-05-13 2010-02-25 Qualcomm Incorporated Interference cancellation under non-stationary conditions
US20100097955A1 (en) * 2008-10-16 2010-04-22 Qualcomm Incorporated Rate determination
US20100310026A1 (en) * 2009-06-04 2010-12-09 Qualcomm Incorporated Iterative interference cancellation receiver
US20110051859A1 (en) * 2009-09-03 2011-03-03 Qualcomm Incorporated Symbol estimation methods and apparatuses
US20110051864A1 (en) * 2009-09-03 2011-03-03 Qualcomm Incorporated Multi-stage interference suppression
US9055545B2 (en) 2005-08-22 2015-06-09 Qualcomm Incorporated Interference cancellation for wireless communications
US9071344B2 (en) 2005-08-22 2015-06-30 Qualcomm Incorporated Reverse link interference cancellation
US9160577B2 (en) 2009-04-30 2015-10-13 Qualcomm Incorporated Hybrid SAIC receiver
US9509452B2 (en) 2009-11-27 2016-11-29 Qualcomm Incorporated Increasing capacity in wireless communications
US9673837B2 (en) 2009-11-27 2017-06-06 Qualcomm Incorporated Increasing capacity in wireless communications

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB0016938D0 (en) * 2000-07-10 2000-08-30 Verticalband Limited Adaptive blind equaliser
US7212566B2 (en) 2002-06-26 2007-05-01 Nokia Corporation Apparatus, and associated method, for performing joint equalization in a multiple-input, multiple-output communication system
CN101834610B (en) 2003-10-06 2013-01-30 数字方敦股份有限公司 Method and device for receiving data transmitted from source through communication channel
KR101329145B1 (en) 2007-10-05 2013-11-21 포항공과대학교 산학협력단 Method of space block coding signal transmission and receive with interactive multiuser detection, and aparatus using the same
CN102882816B (en) * 2012-07-09 2016-04-13 京信通信系统(广州)有限公司 A multi-channel signal equalization method and device
US9263279B2 (en) * 2013-04-17 2016-02-16 Qualcomm Incorporated Combining cut mask lithography and conventional lithography to achieve sub-threshold pattern features

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5432818A (en) * 1993-02-16 1995-07-11 Lou; Yuang Method and apparatus of joint adaptive channel encoding, adaptive system filtering, and maximum likelihood sequence estimation process by means of an unknown data training
US5502735A (en) * 1991-07-16 1996-03-26 Nokia Mobile Phones (U.K.) Limited Maximum likelihood sequence detector

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5557645A (en) * 1994-09-14 1996-09-17 Ericsson-Ge Mobile Communications Inc. Channel-independent equalizer device

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5502735A (en) * 1991-07-16 1996-03-26 Nokia Mobile Phones (U.K.) Limited Maximum likelihood sequence detector
US5432818A (en) * 1993-02-16 1995-07-11 Lou; Yuang Method and apparatus of joint adaptive channel encoding, adaptive system filtering, and maximum likelihood sequence estimation process by means of an unknown data training

Cited By (58)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6044111A (en) * 1996-04-12 2000-03-28 U.S. Philips Corporation Equalizer with a sequence estimation method with state reduction for a receiver in a digital transmission system
WO1999055047A1 (en) * 1998-04-17 1999-10-28 Trustees Of Tufts College Blind adaptive equalization using cost function
US6049574A (en) * 1998-04-17 2000-04-11 Trustees Of Tufts College Blind adaptive equalization using cost function that measures dissimilarity between the probability distributions of source and equalized signals
US6345076B1 (en) * 1998-05-30 2002-02-05 U.S. Phillips Corporation Receiver for a digital transmission system
US6771722B2 (en) * 1998-07-31 2004-08-03 Motorola, Inc. Channel estimator and method therefor
US6611494B1 (en) * 1999-05-19 2003-08-26 Agere Systems Inc. Orthogonal sequence generator
US6512802B1 (en) * 1999-09-28 2003-01-28 Nortel Networks Limited Method and apparatus for equalization and data symbol detection for MPSK modulation
US7110923B2 (en) 1999-11-04 2006-09-19 Verticalband, Limited Fast, blind equalization techniques using reliable symbols
US20020037062A1 (en) * 1999-11-04 2002-03-28 Eilon Riess Reliable symbols as a means of improving the performance of information transmission systems
US7085691B2 (en) 1999-11-04 2006-08-01 Verticalband, Limited Reliable symbols as a means of improving the performance of information transmission systems
US7143013B2 (en) 1999-11-04 2006-11-28 Verticalband, Limited Reliable symbols as a means of improving the performance of information transmission systems
US20030016769A9 (en) * 1999-11-04 2003-01-23 Verticalband, Limited Fast, blind equalization techniques using reliable symbols
US20020007257A1 (en) * 1999-11-04 2002-01-17 Eilon Riess Reliable symbols as a means of improving the performance of information transmission systems
US20020080896A1 (en) * 1999-11-04 2002-06-27 Verticalband, Limited Fast, blind equalization techniques using reliable symbols
US20040028154A1 (en) * 1999-11-12 2004-02-12 Intel Corporaton Channel estimator
US6603823B1 (en) * 1999-11-12 2003-08-05 Intel Corporation Channel estimator
US7366259B2 (en) * 1999-11-12 2008-04-29 Intel Corporation Channel estimator
WO2002021784A1 (en) * 2000-09-06 2002-03-14 Motorola Inc. Soft-output error-trellis decoder for convolutional codes
US20020114408A1 (en) * 2000-12-21 2002-08-22 Nokia Corporation Method for channel equalization, a receiver a channel equalizer, and a wireless communication device
US7245670B2 (en) * 2000-12-21 2007-07-17 Nokia Corporation Method for channel equalization, a receiver a channel equalizer, and a wireless communication device
WO2002087179A2 (en) * 2001-04-18 2002-10-31 Verticalband Limited Blind equalization using reliable symbols
WO2002087179A3 (en) * 2001-04-18 2003-01-16 Eilon Riess Blind equalization using reliable symbols
US7085332B2 (en) 2001-12-14 2006-08-01 Ericsson, Inc. Method and apparatus for two-user joint demodulation in a system having transmit diversity
WO2003052957A1 (en) 2001-12-14 2003-06-26 Ericsson, Inc. Method and apparatus for two-user joint demodulation in a system having transmit diversity
US20030112775A1 (en) * 2001-12-14 2003-06-19 Molnar Karl James Method and apparatus for two-user joint demodulation in a system having transmit deversity
US20040032347A1 (en) * 2002-04-26 2004-02-19 Masato Yamazaki Soft-output decoder with computation decision unit
US20050052293A1 (en) * 2002-04-26 2005-03-10 Masato Yamazaki Soft-output decoder with computation decision unit
US6879267B2 (en) * 2002-04-26 2005-04-12 Oki Electric Industry Co., Ltd. Soft-output decoder with computation decision unit
US20050111347A1 (en) * 2002-05-10 2005-05-26 Marco Breiling Transmitting device and receiving device
US7372802B2 (en) * 2002-05-10 2008-05-13 Fraunhofer - Gesellschaft Zur Foerderung Der Angewandten Forschung E.V. Message communication via channels having strong fading
US9071344B2 (en) 2005-08-22 2015-06-30 Qualcomm Incorporated Reverse link interference cancellation
US9055545B2 (en) 2005-08-22 2015-06-09 Qualcomm Incorporated Interference cancellation for wireless communications
US8234549B2 (en) * 2008-03-06 2012-07-31 Nec Laboratories America, Inc. Simultaneous PMD compensation and chromatic dispersion compensation using LDPC coded OFDM
US20090228766A1 (en) * 2008-03-06 2009-09-10 Nec Laboratories America, Inc. Simulatenous PMD Compensation and Chromatic Dispersion Compensation Using LDPC Coded OFDM
US20100046660A1 (en) * 2008-05-13 2010-02-25 Qualcomm Incorporated Interference cancellation under non-stationary conditions
US8675796B2 (en) 2008-05-13 2014-03-18 Qualcomm Incorporated Interference cancellation under non-stationary conditions
US20090303976A1 (en) * 2008-06-09 2009-12-10 Qualcomm Incorporated Increasing capacity in wireless communication
US20090303968A1 (en) * 2008-06-09 2009-12-10 Qualcomm Incorporation Increasing capacity in wireless communications
US9408165B2 (en) 2008-06-09 2016-08-02 Qualcomm Incorporated Increasing capacity in wireless communications
US20090304024A1 (en) * 2008-06-09 2009-12-10 Qualcomm Incorporated Increasing capacity in wireless communications
US8995417B2 (en) 2008-06-09 2015-03-31 Qualcomm Incorporated Increasing capacity in wireless communication
US9014152B2 (en) 2008-06-09 2015-04-21 Qualcomm Incorporated Increasing capacity in wireless communications
US9237515B2 (en) 2008-08-01 2016-01-12 Qualcomm Incorporated Successive detection and cancellation for cell pilot detection
US9277487B2 (en) 2008-08-01 2016-03-01 Qualcomm Incorporated Cell detection with interference cancellation
US20100029262A1 (en) * 2008-08-01 2010-02-04 Qualcomm Incorporated Cell detection with interference cancellation
US20100029213A1 (en) * 2008-08-01 2010-02-04 Qualcomm Incorporated Successive detection and cancellation for cell pilot detection
US20100046595A1 (en) * 2008-08-19 2010-02-25 Qualcomm Incorporated Semi-coherent timing propagation for geran multislot configurations
US8509293B2 (en) 2008-08-19 2013-08-13 Qualcomm Incorporated Semi-coherent timing propagation for GERAN multislot configurations
US20100097955A1 (en) * 2008-10-16 2010-04-22 Qualcomm Incorporated Rate determination
US9160577B2 (en) 2009-04-30 2015-10-13 Qualcomm Incorporated Hybrid SAIC receiver
US20100310026A1 (en) * 2009-06-04 2010-12-09 Qualcomm Incorporated Iterative interference cancellation receiver
US8787509B2 (en) 2009-06-04 2014-07-22 Qualcomm Incorporated Iterative interference cancellation receiver
US20110051864A1 (en) * 2009-09-03 2011-03-03 Qualcomm Incorporated Multi-stage interference suppression
US20110051859A1 (en) * 2009-09-03 2011-03-03 Qualcomm Incorporated Symbol estimation methods and apparatuses
US8831149B2 (en) * 2009-09-03 2014-09-09 Qualcomm Incorporated Symbol estimation methods and apparatuses
US8619928B2 (en) 2009-09-03 2013-12-31 Qualcomm Incorporated Multi-stage interference suppression
US9673837B2 (en) 2009-11-27 2017-06-06 Qualcomm Incorporated Increasing capacity in wireless communications
US9509452B2 (en) 2009-11-27 2016-11-29 Qualcomm Incorporated Increasing capacity in wireless communications

Also Published As

Publication number Publication date Type
CN1278382A (en) 2000-12-27 application
WO1999023795A1 (en) 1999-05-14 application
CA2308173A1 (en) 1999-05-14 application
KR20010031665A (en) 2001-04-16 application
EP1029409A1 (en) 2000-08-23 application
JP4329974B2 (en) 2009-09-09 grant
DE69839967D1 (en) 2008-10-16 grant
EP1029409B1 (en) 2008-09-03 grant
JP2001522197A (en) 2001-11-13 application

Similar Documents

Publication Publication Date Title
Qureshi Adaptive equalization
Kaleh et al. Joint parameter estimation and symbol detection for linear or nonlinear unknown channels
US5465276A (en) Method of forming a channel estimate for a time-varying radio channel
US5455839A (en) Device and method for precoding
US5432816A (en) System and method of robust sequence estimation in the presence of channel mismatch conditions
US4885757A (en) Digital adaptive receiver employing maximum-likelihood sequence estimation with neural networks
US5278871A (en) Method and apparatus for estimating signal weighting parameters in a receiver
US5909465A (en) Method and apparatus for bidirectional demodulation of digitally modulated signals
US5513214A (en) System and method of estimating equalizer performance in the presence of channel mismatch
Raheli et al. Per-survivor processing: A general approach to MLSE in uncertain environments
US5081651A (en) Maximum likelihood sequence estimation apparatus
Ungerboeck Adaptive maximum-likelihood receiver for carrier-modulated data-transmission systems
Yeh et al. Adaptive minimum bit-error rate equalization for binary signaling
US5577068A (en) Generalized direct update viterbi equalizer
US6674795B1 (en) System, device and method for time-domain equalizer training using an auto-regressive moving average model
US5231648A (en) Adaptive equalizer for digital cellular radio
US5673294A (en) Adaptive maximum likelihood sequence estimation apparatus and adaptive maximum likelihood sequence estimation method
US20040081074A1 (en) Signal decoding methods and apparatus
US5263053A (en) Fractionally spaced maximum likelihood sequence estimation receiver
EP0425458A1 (en) A method of adapting a viterbi algorithm to a channel having varying transmission properties, and apparatus for carrying out the method
US5867538A (en) Computational simplified detection of digitally modulated radio signals providing a detection of probability for each symbol
US6269131B1 (en) Physical channel estimator
US6154507A (en) System and method for signal demodulation
US6590932B1 (en) Methods, receiver devices and systems for whitening a signal disturbance in a communication signal
Chen et al. Adaptive Bayesian equalizer with decision feedback

Legal Events

Date Code Title Description
AS Assignment

Owner name: ERICSSON, INC., NORTH CAROLINA

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:MOLNAR, KARL;REEL/FRAME:008873/0523

Effective date: 19971031

CC Certificate of correction
FPAY Fee payment

Year of fee payment: 4

REMI Maintenance fee reminder mailed
FPAY Fee payment

Year of fee payment: 8

FPAY Fee payment

Year of fee payment: 12

AS Assignment

Owner name: CLUSTER LLC, DELAWARE

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:ERICSSON INC.;REEL/FRAME:030192/0273

Effective date: 20130211

AS Assignment

Owner name: UNWIRED PLANET, LLC, NEVADA

Effective date: 20130213

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:CLUSTER LLC;REEL/FRAME:030201/0389

AS Assignment

Owner name: CLUSTER LLC, SWEDEN

Free format text: NOTICE OF GRANT OF SECURITY INTEREST IN PATENTS;ASSIGNOR:UNWIRED PLANET, LLC;REEL/FRAME:030369/0601

Effective date: 20130213